Property Energy Disaggregation Using Meter Data and Context
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Solution Overview
Problem
Existing methods for monitoring energy consumption in properties require sensors connected to appliances, which can be difficult to obtain and may not be feasible in all cases.
Innovation Solution
A method and system that receive energy consumption data from electricity and gas meters and context information to determine variations indicative of device switching, classify events, and associate energy consumption with predetermined categories without the need for appliance-specific sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are connected to appliances for monitoring energy consumption, then measurement precision is improved, but device complexity and ease of operation worsen due to installation difficulty
Solution Approach 1:
The patent uses existing electricity and gas meters as intermediary devices to capture energy consumption data without requiring direct sensor installation on each appliance. These meters serve as mediators that indirectly measure the energy consumption of multiple appliances through the main supply lines, eliminating the need for intrusive sensor deployment while maintaining measurement capability.
Solution Approach 2:
The system makes existing meters (electricity and gas) perform an additional function beyond their traditional billing purpose. These meters not only measure total energy consumption for billing but also serve as data collection points for appliance-level energy monitoring, enabling multiple functions from single existing devices.
2Measurement precision
If sensors are installed on appliances for energy monitoring, then data accuracy is improved, but ease of operation worsens due to difficult data acquisition
Solution Approach 1:
The system leverages the self-service capability of existing meter infrastructure. The meters automatically record and store energy consumption data without requiring manual intervention or complex data extraction processes. The data is already available in a structured format within the meter systems, eliminating the need for additional data acquisition efforts.
Solution Approach 2:
The meters act as intermediaries that already possess the required energy consumption data as part of their normal operation. By querying these meters, the system obtains accurate energy data without needing to install separate sensing and data collection infrastructure on each appliance.
3Measurement precision
If appliance-specific sensors are used for monitoring, then measurement precision is improved, but adaptability worsens due to limited applicability across different properties
Solution Approach 1:
The system achieves universal applicability by using standard electricity and gas meters that exist in virtually all properties. These meters can be queried across different property types without requiring customization or special installation, making the solution broadly adaptable while still enabling precise energy consumption monitoring through intelligent data analysis.
Solution Approach 2:
The system segments the energy consumption data by analyzing variations and patterns in the aggregate meter readings to identify individual appliance consumption. This segmentation approach allows the system to derive appliance-level insights from property-level meter data, maintaining precision without requiring property-specific sensor deployment.
Data Source
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AI summary
In one aspect, there is described a method having: receiving energy consumption data from one or more electricity and/or gas meters associated with a property having one or more devices having larger devices and/or smaller devices; receiving context information data about the property; retrospectively determining, in the received energy consumption data, one or more variations indicative of consumption of the devices; identifying one or more events associated with the devices, based on the determined variations; classifying the identified events into predetermined sub-categories associated with the devices, based on the energy consumption data and the context information data; and associating a proportion of the received energy consumption data to respective predetermined categories associated with the devices, based on the classification in the sub-categories.